Building Robust ETL Systems for Data Analytics in Telecom

Authors

  • Harish Goud Kola  Independent Researcher, USA

DOI:

https://doi.org//10.32628/CSEIT1952292

Keywords:

ETL Systems, Telecom Data Analytics, Data Integration, IBM DataStage, Talend, Predictive Analytics, Customer Churn, Inventory Management, Supply Chain Optimization, Machine Learning Models

Abstract

This paper explores the development of robust ETL (Extract, Transform, Load) systems for telecom data analytics, emphasizing their importance in managing large volumes of diverse datasets generated within the industry. The study highlights how well-designed ETL systems enhance decision-making, operational efficiency, and customer satisfaction by enabling seamless data integration, transformation, and analysis. Through a comparison of ETL tools like IBM DataStage and Talend, the paper identifies strengths, challenges, and future trends in telecom analytics. Furthermore, it discusses predictive analytics, inventory management, and supply chain optimization powered by ETL systems, offering insights into the evolving role of data analytics in telecom.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2019-06-10

Issue

Volume 5, Issue 3, May-June-2019

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Harish Goud Kola, " Building Robust ETL Systems for Data Analytics in Telecom , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 3, pp.694-700, May-June-2019. Available at doi : https://doi.org/10.32628/CSEIT1952292